تولید لیپاز توسط باکتری اعماق دریایی Idiomarina zobellii؛ شناسایی فاکتورهای موثر و بهینه‌سازی فرایند تولید

نوع مقاله: پژوهشی- انگلیسی

نویسندگان

1 دانشجوی کارشناسی ارشد، دانشگاه آزاد اسلامی، واحد ورامین- پیشوا، ورامین، ایران

2 استادیاربیوشیمی و بیوفیزیک، دانشگاه آزاد اسلامی، واحد ورامین- پیشوا، ورامین، ایران

3 استادیار میکروبیولوژه، دانشگاه آزاد اسلامی، واحد ورامین- پیشوا، ورامین، ایران

چکیده

مقدمه: در این مطالعه باکتری Idiomarinazobellii(باکتری اعماق دریایی) به عنوان منبع جدید برای تولید لیپاز مورد استفاده قرارگرفت. لیپازهای بدست آمده توسط باکتری­های هالوفیل برای کاربردهای صنعتی و بیوتکنولوژیک بسیار جذاب می­باشند.
مواد و روش‏‏ها: دما و pH بهینه فعالیت لیپاز Idiomarinazobelliiبا اندازه­گیری میزان فعالیت در محدوده دمایی 30 تا60 درجه سانتی‌‌گراد و محدوده pH 7 تا 11 تعیین­شد. برای تعیین فاکتورهای موثر بر تولید لیپاز، فاکتورهای مورد نظر توسط روش پلاکت-برمن ارزیابی شد. شرایط بهینه تولید بر اساس فاکتورهای موثر با استفاده از روش طرح مرکب مرکزی تعیین و در نهایت، میزان افزایش در تولید لیپاز تحت شرایط بهینه با شرایط اولیه مقایسه شد.
نتایج: دما و pH بهینه آنزیم به ترتیب 50 و 8-7 درجه سانتی‌‌گراد تعیین­شد. از بین 10 فاکتور مورد بررسی، غلظت کلریدمنیزیم، مخمر، بی­کربنات­سدیم، نیترات‌آمونیوم و دما به عنوان فاکتورهای موثر شناسایی شدند. شرایط بهینه برای این فاکتورها بصورت زیر تعیین­شد: کلریدمنیزیم (g/L18/7)، مخمر (g/L 1/1)، بی­کربنات­سدیم (g/L 11/0)، نیترات‌آمونیوم (g/L 65/3) و دما 42 درجه سانتی‌‌گراد. میزان فعالیت آنزیمی بدست آمده از U/ml39/0 به U/ml 8/0 تحت شرایط بهینه افزایش یافت.
بحث و نتیجهگیری: لیپاز بدست آمده از Idiomarinazobelliiرا می­توان جزء لیپازهای قلیادوست طبقه­بندی کرد. با توجه به اهمیت لیپازهای قلیادوست و افزایش 9/2 برابری تولید آنزیم تحت شرایط بهینه­سازی­شده، می­توان مقدار کافی آنزیم لیپاز از Idiomarinazobelliiبرای استفاده در کاربردهای صنعتی و بیوتکنولوژیک بدست آورد.

کلیدواژه‌ها

موضوعات


عنوان مقاله [English]

Production of lipase by the deep-sea bacterium Idiomarina zobellii; Identification of effective factors and optimization of production process

نویسندگان [English]

  • Shahrbanoo Sadeghi-Dastjerdi 1
  • Mehdi Ebrahimi 2
  • Rozbeh Yalfani 3
1 M.Sc student, Department of Biochemistry and Biophysics, Faculty of Biological sciences, Varamin-Pishva Branch, Islamic Azad University, Varamin, Iran
2 Assistant Professor, Department of Biochemistry and Biophysics, Faculty of Biological sciences, Varamin-Pishva Branch, Islamic Azad University, Varamin, Iran
3 Assistant Professor, Department of Biochemistry and Biophysics, Faculty of Biological sciences, Varamin-Pishva Branch, Islamic Azad University, Varamin, Iran
چکیده [English]

Introduction: In this study, the deep-sea bacterium, Idiomarina zobellii, was used as a new source for the production of lipase. Lipases from halophilic bacteria are attractive for use in diverse industrial and biotechnological applications.
Materials and methods: The optimal temperature and pH for the activity of I. zobellii lipase was determined using different temperature and pH ranging from 30-60 °C and 7-11, respectively. The growth conditions influencing lipase production were determined using two-level fractional factorial Plackett–Burman design. The optimum levels of effective factors for production process were determined by using the response surface central composite design.
Results: The optimum temperature and pH of I. zobellii lipase in the crude mixture were 7-8 and 50 °C, respectively. Among the screened 10 factors, MgCl2, yeast extract, sodium bicarbonate, ammonium nitrate concentrations and temperature were found to be effectively important. The highest productivity level was achieved at 42°C temperature and MgCl2 7.18 (g/L), yeast extract 1.1 (g/L), NH4NO3 3.65 (g/L) and NaHCO3 0.11 (g/L) concentrations. When the predicted optimum levels of effective factors were employed, the enzyme activity almost doubled (0.8 comparing to 0.39 U/ml) of the initial.
Discussion and conclusion: The Idiomarina zobellii originated lipase is an  alkalophile enzyme. A significant increase in enzyme production, as large as  2.09 folds, is achievable   under optimized conditions that favours biotechnology and industrial applications.

کلیدواژه‌ها [English]

  • Lipase
  • Idiomarina zobellii
  • Plackett–Burman Design
  • Response Surface Methodology (RSM)
  • Deep-sea Bacterium

Introduction

Lipases (triacylglycerol acylhydrolases, E.C. 3.1.1.3) catalyze the hydrolysis of fats into fatty acids and glycerol and also esterification reaction for the production of fats. Lipases can perform these reactions at the water-insoluble substrate interface (1). In addition, lipases have the ability to catalyze inter esterification, acidolysis, alcoholysis and aminolysis reactions. In addition to their broad substrate specificity, lipases carry  properties such as good chemoselectivity, regioselectivity and enantioselectivity (2). This wide range of biochemical specifications has  made lipases one of the indispensable components in different biotechnological fields including synthesis of biopolymers, biodiesel, the production of enantio-pure pharmaceuticals, agro-chemicals, and flavour compounds (3). These applications have made lipases one of the most consumed and economical important enzymes in the world enzyme market. This growth in daily applications and demands in new biotechnological and industrial applications have led to daily efforts to discover new lipases with different specificities. Therefore, characterization of more natural sources of lipases holding newer properties is increasingly demanded (4). Many microorganisms and higher eukaryotes can naturally produce lipases. Among microorganisms, bacteria, fungi, yeasts and actinomyces are the most important lipase producers. Microbial lipases are most commercially useful and widely used in various industries (4, 5).

Over the last few decades, microorganisms from extreme marine conditions such as high pressures, hypersaline habitats and extreme temperature have increasingly received  interest for industrial processes due to their high potentials in the production of new bio-products including enzymes and other active metabolites (6-8). Therefore, studies focusing on optimization of growth conditions and production of bioactive products such as enzymes from marine microorganisms are attractive and on demand for biotechnological and pharmaceutical industries. In addition, enzymes from psychrophilic micro-organisms effectively function in the cold, and the biotechnological applications of these cold-adapted enzymes have been considered in the last few decades (9).

Idiomarina zobellii is a gram-negative, psychrotolerant, heterotrophic, aerobic, rod-shaped bacterium which was isolated from the seawater of north-western Pacific Ocean at the depths of 4000-5000 m. Growth occurs at temperature of 4−30°C with its optimum at 20−22°C. The pH range for growth is 5.5−9.5 with its optimum at 7.5−8. This deep-sea bacterium requires NaCl (0.6−15%) for growth, hence it is regarded as moderate halophile (10). Enzymes from halophilic microorganisms such as I. zobellii can survive  at high salt concentrations (11). These adaptations besides the existing robust nature of lipases, make halophilic lipases more attractive for industrial and biotechnological applications. (12). The number of studies on lipase producing halophilic organisms however, is limited. To date, there is more attention to these halophilic microorganisms, especially halo-archaea and their applicability for lipase production (13-15). Submerged fermentation is commonly used for the production of enzymes and other bio-active compounds by micro-organisms (5, 6). Growth conditions such as availability of carbon and nitrogen sources, the presence of activators, stimulators, inhibitors, surfactants, incubation temperature, pH, and the level and source of inoculum can influence the production of lipase (16). Besides, these various factors, the dependency of a microorganism for lipase production varies from one microorganism to another. Moreover, most of the culture mediums and conditions proposed for optimal growth of microorganisms may be improper for the production of lipase. Therefore, the optimization of production conditions is an important step in the development of economically feasible bioprocesses (17).

The one factor at a time method, is a time consuming and costly method to distinguish factors affecting the process; therefore, it has been replaced with Plackett–Burman design (PBD) method (18). After determining the effective factors, Response Surface Methodology (RSM) could be used to investigate the combinatorial interactions of medium components and define the optimum conditions of effective factors for appropriate responses (17).

In this study, for the first time, the nutritional and cultural parameters affecting the production of lipase from a deep-sea bacterium, I. zobellii were investigated using PBD. These factors were used for optimization of growth condition by applying RSM to gain higher lipase production. Some properties such as optimum pH and temperature of lipase from I. zobelli were also investigated.

 

Materials and Methods

Microorganism and Preparation of inoculum: Live active culture of I. zobelli in a glass plate was obtained from the Iranian Biotechnology Research Center (IBRC-M10598). For pre-culture, a loop full of the bacterial active colony was added aseptically to 25 ml of Marine broth pH 7.5 and the bacterial suspension was incubated at 22°C for 48 h with 120 rpm shaking. A volume of 50 ml of Marine broth as preparation medium collected in a 250 ml Erlenmeyer flask was inoculated with 1 ml of the 48 h old culture and incubated as previously described. The optical density of the culture at 600 nm was recorded every 24 h to obtain the growth kinetics of I. zobellii. Using fresh culture medium, load of the bacterial growth was adjusted at OD=1. This preparation was subsequently used for inoculation of the experimental flasks.

Lipase assay: p-Nitrophenyl palmitate was used as a substrate for the measurement of I. zobellii lipase activity (19). To prepare the substrate solution, 10 ml of p-nitrophenyl palmitate (0.1 M) ethanol solution was added to a mixture of 207 mg of sodium deoxycholate and 100 mg of gum Arabic in 90 ml phosphate buffer (0.05 M, pH 6.5). To start the reaction, 0.1 ml of crude enzyme extract or blank solution (50 mM phosphate buffer, pH 6.5) was added to 2.4 ml of substrate solution. After 15 min incubation in 50 mM phosphate buffer, absorbance at 410 nm was recorded. One enzyme unit (U) was defined as the lipase activity that liberates 1μmol of p-nitrophenyl palmitateper milliliter per minute under standard assay conditions (20).

Temperature and pH effect on the lipase activity: The optimal temperature for the activity of I. zobellii lipase was determined using different temperatures ranging from 30-60°C (with 5°C intervals). For this, the substrate solution was mixed with crude enzyme extract, incubated at the selected temperatures for 15 min with the absorbance level recorded at 410 nm. The pH dependency of I. zobellii lipase was determined using substrate solution with different pH ranging from 7-11 with 0.5 unit intervals in the following buffers: 0.5 M sodium phosphate buffer (7-8.5), 0.5 M Britton-Robinson Buffer (9-11).

Plackett–Burman design: Factors affecting the production of lipase by I. zobellii were determined with experimental designed using the two factorial Plackett–Burman (PB) methods. In this method, n+1 experimental design for n variables and interactions between the different variables were explained statistically (21). Among medium components and culture conditions, 10 parameters including ferric citrate (g/L), ammonium nitrate (g/L), sodium bicarbonate (g/L), yeast extract (g/L), peptone (g/L), MgCl2 (M), shaking (rpm), pH, incubation temperature (°C) and time (h) were investigated. All experimental patterns and analysis were designed using the statistical software package “Minitab 17” (Stat-Ease Inc., USA) in 2 levels for each factor, high (+) and low (–), with one center point. According to selected levels for each variable, a collection of 15 experiments were designed. All experiments were performed in triplicate and the average was analyzed using Minitab software. Table (1) shows variables and selected levels for each experimental design.

The PB design is based on the following first-order polynomial equation:

Y = ßo + Σ ßi Xi

Where, Y, is the lipase activity; ßo, is the model intercept; ßi, is the linear coefficient; Xi, is the level of the independent variable.

Optimization using response surface methodology (RSM): RSM methodology was used for further optimization of significant variables obtained by Plackett-Burman design. According to screening experiments, among investigated variables, MgCl2 medium, yeast extract, sodium bicarbonate, ammonium nitrate and the temperature were determined as significant. These variables were used in a Central Composite Design (CCD) to explore their effect on lipase production. A collection of 32 different experiments for three variables each in 5 levels (Table 2) was designed. This design consists of 16 cube points, 6 center points in cube and 10 axial point runs with 6 replications at the center point to evaluate the pure error. The lipase activity (U/ml) of each run was taken as response and the following second order polynomial equation was used to determine the relationship among variables:

Y = ßo + Σ ßi xi + Σ ßii xi2 + Σ ßij xixj,

 i = 1, 2, 3... k

Where, Y, is the predicted response; k, is the number of factor variables; ßo, is the model constant; ßi, is the linear coefficient; ßii, is the quadratic coefficient; ßij, is the interaction coefficient.

Model validation: The Fischer’s F-test was used to determine the statistical significance of polynomial model and the coefficient of determination R2 value was checked for the quality of represented model (17, 22).

Optimization of variable’s level: The final goal of optimization process was to create an increment in lipase production by a combination of factor levels. To achieve this, predicted levels for each variable were used in an experiment. The predicted levels of variables were as follows:  temperature 42 °C, MgCl2 7.18 (g/L), yeast extract 1.1 (g/L), NH4NO3 3.65 (g/L) and NaHCO3 0.11 (g/L). The predicted lipase activity by the model in 95% confidence level was 3.78 U/ml. The “Minitab 17” (Stat-Ease Inc., USA) software package was used for experimental design and statistical analysis of the responses.

 

Results

Inoculum preparation: Inoculum size and growth profile of bacterial culture can affect enzyme production. The number of bacterial cells in the production media determines the availability of nutrients. The concentration of growth inhibitors in culture increases, with increase in culture time (23). To overcome these effects in lipase production, growth profile of I. zobellii was obtained in order to realize a constant amount of active bacteria for use as an inoculum in all experiments. According to Fig. 1, absorbance at 600 nm increased up to 1.7 for 48 h and then remained nearly constant. After 72 h, absorbance decreases as a result of the death of bacteria. About 1 ml of a 24 h bacterial culture was used with absorbance of 0.8 at 600 nm to inoculate culture medium in all experiments.

Optimization of enzyme assay: It is important to assay each enzyme in its optimum condition. Temperature and pH are two important assay conditions, which can affect enzyme activity. Lipase activities in different pH and temperatures were investigated to obtain the optimum condition of the assay (Fig. 2). The pH profile shows that I. zobellii lipase has the maximum activity at pH 7-8. According to Figure 2b, the optimum temperature for I. zobellii lipase was determined as 50°C.

Detection of significant factors using Plackett–Burman design: Different medium components and culture conditions including ferric citrate, ammonium nitrate, sodium bicarbonate, yeast extract, peptone, MgCl2, shaking, pH and incubation temperature, and time were selected as independent variables and were investigated in PB design. Using this method, a set of 15 different experiments were designed. Results of these experiments with respect to lipase activity were shown in Table 1. The following equation shows the effect of each studied variable in lipase production from I. zobellii as a mathematical model:

Activity (U/ml) = -1.712 + 0.0277 temperature+ 0.01 pH - 0.00083 Incubation time - 0.00017 Shaking+ 0.0250 peptone + 0.708 yeast extract + 0.0918 MgCl2 - 2.29 NaHCO3 + 0.224 [NH4]NO3+ 1.08 ferric citrate + 0.017 Ct Pt

 

Fig. 1- Growth curve of I. zobellii during different incubation time

 

 

 

Fig. 2- Activity of I. zobellii lipase in different pH (a) and temperatures (b)

 

Table 1- The Placket-Burman design for detection of significant factors affecting lipase production in I. zobellii

 

Variables

Lipase activity (U/ml)

Run order

Temperature (°C)

pH

Shaking (rpm)

Incubation time (h)

NH4NO3 (g/L)

MgCl2 (g/L)

Yeast extract (g/L)

Peptone (g/L)

Ferric citrate (g/L)

NaHCO3 (g/L)

Observed

Predicted

1

26.4

6.5

80

72

1.92

7.08

0.8

6.25

0.08

0.192

6.50

10.13

2

26.4

8.5

160

24

1.92

4.72

0.8

3.75

0.12

0.192

8.62

5.43

3

17.6

8.5

80

72

1.92

4.72

0.8

3.75

0.12

0.128

2.74

4.30

4

26.4

6.5

160

72

1.28

4.72

1.2

3.75

0.12

0.128

11.21

14.07

5

26.4

8.5

160

24

1.28

7.08

0.8

6.25

0.08

0.128

8.93

11.41

6

26.4

8.5

80

72

1.28

4.72

1.2

3.75

0.08

0.192

12.62

9.84

7

17.6

8.5

160

72

1.92

7.08

1.2

5

0.08

0.128

18.26

16.37

8

17.6

6.5

160

72

1.28

7.08

0.8

6.25

0.12

0.192

4.94

1.82

9

17.6

6.5

160

24

1.92

4.72

1.2

6.25

0.08

0.192

4.54

7.44

10

26.4

6.5

80

24

1.92

7.08

1.2

3.75

0.12

0.128

25.39

21.73

11

17.6

8.5

80

24

1.28

7.08

1.2

5

0.12

0.192

7.29

9.19

12

17.6

6.5

80

24

1.28

4.72

0.8

5

0.08

0.128

3.37

1.35

13

22

7.5

120

48

1.6

5.9

1

3.75

0.1

0.16

9.17

9.42

14

22

7.5

120

48

1.6

5.9

1

6.25

0.1

0.16

8.54

9.42

15

22

7.5

120

48

1.6

5.9

1

6.25

0.1

0.16

9.25

9.42

 

 

The F- and P-values of the given model were 7.78 and 0.004, respectively, so the model seems significant. The confidence of determination (R2) of PB design is 81.2%. This means that the represented mathematical model can fit 81.2% of total variables in the range of studied values.

The significance of each variable at the designed confidence level was determined with p-value and F-value of ANOVA analysis. The significant variables should have low p-value (less than 0.05) and high f-value (24). Standardized effects of variables were shown in Pareto chart (Fig. 3). According to this chart, five parameters including ammonium nitrate, sodium bicarbonate, yeast extract, MgCl2, and incubation temperature were determined as significant. This implies that ferric citrate and peptone used in the culture of I. zobellii as carbon and nitrogen sources have no effects on lipase production and could be replaced by other components. Among three significant factors, yeast extract and ammonium nitrate have the highest and lowest effects, respectively.

Optimization of lipase production using RSM: The Response Surface Methodology (RSM) can be used to investigate interactions between significant parameters in the production of lipase. In this study, significant variables determined with PB design (temperature, yeast extract, sodium bicarbonate, ammonium nitrate and MgCl2) were used to inquire the combined effects of these independent variables. A set of 32 experiments were designed using five levels for each parameter in the CCD.

 

 

Fig. 3- Pareto chart of the standardized effect of variables for detection of significant factors for lipase production from I. zobellii.

 

 

 

Table 2 represents observed and predicted values for each experiment in the CCD. The observed lipase activity varied widely from 1.18 to 3.96 U/ml among different experiments. The following second order polynomial equation represents linear, square and 2-way interaction effects in the production of lipase.

Lipase activity (U/ml)= -19.11 + 0.054 A + 2.24 B + 15.55 C - 0.181 D + 37.8 E + 0.000205 A*A - 0.1409 B*B - 4.82 C*C - 0.03999 D*D - 212.5 E*E - 0.00233 A*B - 0.0312 A*C + 0.00164 A*D - 0.131 A*E - 0.477 B*C + 0.0748 B*D + 1.25 B*E - 0.186 C*D + 4.9 C*E + 0.63 D*E

Where, A; temperature, B; MgCl2, C; yeast extract, D; [NH4]NO3 and E; NaHCO3.

 

 

 

Table 2- Data for the central composite design of significant factors in the production of lipase from I. zobellii.

Std. Order

Variables

Lipase activity (U/ml)

Temperature (°C)

MgCl2 (g/L)

Yeast (g/L)

[NH4]N03 (g/L)

NaHCO3 (g/L)

Observed

Predicted

1

27

6.49

1.1

1.76

0.14

1.78

2.02

2

37

6.49

1.1

1.76

0.11

2.73

2.94

3

27

7.67

1.1

1.76

0.11

1.96

2.21

4

37

7.67

1.1

1.76

0.14

1.39

1.78

5

27

6.49

1.3

1.76

0.11

2.88

2.88

6

37

6.49

1.3

1.76

0.14

2.07

2.21

7

27

7.67

1.3

1.76

0.14

1.45

1.63

8

37

7.67

1.3

1.76

0.11

1.62

1.76

9

27

6.49

1.1

4.96

0.11

1.63

1.78

10

37

6.49

1.1

4.96

0.14

1.54

1.82

11

27

7.67

1.1

4.96

0.14

2.28

2.61

12

37

7.67

1.1

4.96

0.11

2.84

3.14

13

27

6.49

1.3

4.96

0.14

1.18

1.26

14

37

6.49

1.3

4.96

0.11

1.65

1.70

15

27

7.67

1.3

4.96

0.11

1.75

1.83

16

37

7.67

1.3

4.96

0.14

1.57

1.79

17

22

7.08

1.2

3.36

0.125

3.52

3.24

18

42

7.08

1.2

3.36

0.125

3.96

3.47

19

32

5.9

1.2

3.36

0.125

2.49

2.29

20

32

8.26

1.2

3.36

0.125

2.89

2.33

21

32

7.08

1

3.36

0.125

3.43

2.73

22

32

7.08

1.4

3.36

0.125

1.99

1.92

23

32

7.08

1.2

0.16

0.125

1.86

1.46

24

32

7.08

1.2

6.56

0.125

1.45

1.09

25

32

7.08

1.2

3.36

0.095

2.94

2.72

26

32

7.08

1.2

3.36

0.155

2.49

1.94

27

32

7.08

1.2

3.36

0.125

3.01

3.26

28

32

7.08

1.2

3.36

0.125

2.89

3.26

29

32

7.08

1.2

3.36

0.125

3.31

3.26

30

32

7.08

1.2

3.36

0.125

2.86

3.26

31

32

7.08

1.2

3.36

0.125

3.36

3.26

32

32

7.08

1.2

3.36

0.125

3.36

3.26

 

 

The F- and P-values for the model were 2.9 and 0.037, respectively, implying that the model terms are significant. The coefficient of variations (R2) always lies between 0 and 1 and it indicates the ability of the model to explain the variability in the response (25). The stronger model has R2 close to 1 while in a weak model, it is close to zero (26). Considering R2 value (0.8405), demonstrated model can explain 84.05% of the total variability within the range of studied values.

Analysis of Response Surface Methodology (RSM): Interactive effects of two variables at a time on lipase production in I. zobellii were investigated by 3D surface plots. In each plot, one variable remains constant while two other variables change according to defined values in CCD.  The surface of the curved sheet shows the changes in the lipase production. According to the 3D surface plots, different temperatures have no effect on lipase production when other factors remain constant (Fig. 4a, 4b, 4c and 4d). In each given temperature, we can see a maximum lipase production in response to the variations in second factors. Moreover, there was no significant difference in the maximum lipase production in response to the different temperatures. In the presence of different concentrations of MgCl2, a maximum point in lipase production can be seen in response to variations in other factors (Fig. 4e, 4f and 4g). In the presence of different concentrations of NaHCO3 and(NH4)NO3, lipase production decreased when the concentration of yeast extract increased from 1.2 (Fig. 4h and 4i). According to Fig. 4j, there is an optimal concentration of NaHCO3 and  (NH4)NO3 for lipase production.

 

 

Fig. 4- Surface plot for investigation of interactive effects of variables on lipase production.

 

A confirmatory test was performed using the model predicted level of significant parameters and compared with the un-optimized condition. The lipase activity in optimized condition was 3.88 U.ml-1 which was close to the predicted value (3.73 U.ml-1) and was significantly more than un-optimized condition (1.89 U.ml-1).  This result shows that the lipase production under optimized condition increases by nearly 100% and the model can properly define optimal levels of determined variables to increase the lipase production.

 

Discussion and conclusion

Lipases are one of the most usable enzymes with wide applications from different industrial areas such as food, paper, detergent, leather, textile, organic synthesis, cosmetics to pharmaceutical and medical (27). These abundant applications have made lipases one of the most demanded enzymes in global enzyme market. Application of lipases in new processes requires enzymes with new proportional properties. Finding lipase enzymes with new properties is a daily demand, and for this reason, many efforts are focused to find new sources of lipases. Among different sources, microbial lipases are most valuable and widely used.

In this study, an optimization process for increase in the production of lipase enzyme from I. zobellii, a deep-sea bacterium, was investigated. Lipase activity was used to determine the production level of enzyme in each experiment. Optimum pH and temperature of the enzyme in crude mixture was determined to be 7-8 and 50°C, respectively. Most bacterial lipases have maximum activity at pH values higher than 7, and they are known as Alkalophilic (5, 28). The maximum activity of I. zobellii lipase (50°C) is similar to that of other moderate thermophile lipases from Spirulina platensis (45°C), P. camaembertii Thom PG-3 (48 °C) and Aspergillus niger F044 (45 °C) (20, 29, 30).

Submerged culture method is widely used for the production of lipases using different microbial sources (4). In each microbial source, optimal culture condition and nutritional requirements must be determined to increase lipase production. The most important parameters influencing lipase production includes the type and concentration of carbon and nitrogen sources, the growth temperature, the culture pH and agitation (31). Detection of effective parameters in the production of lipase involves the investigation of different levels of each parameter when other parameters are unchanged. This method is time-consuming and costly when a large number of factors need to be investigated. Nowadays, Plackett–Burman design is widely used as a powerful technique for the evaluation and detection of the variables that influence the response (17, 18, 24, 32-34). The strength of this method is a considerable reduction in the number of experiments required to determine significant variables. Culture conditions including incubation time, temperature, pH and shaking directly influence bacterial growth and lipase production. The effect of these factors on lipase production relates to bacterial species. Among these factors, temperature effect on lipase production by I. zobellii was determined as significant. In addition, using PB method, temperature, MgCl2, yeast extract, (NH4)NO3 and NaHCO3 were determined to be effective in lipase production by I. zobellii. Bacterial dependency on the medium component and optimal levels for lipase production differ among bacterial species. In I. zobelli, optimal lipase production temperature (42°C) is significantly more than growth temperature (22°C). In the study reported by Esmaeili, the optimum production and growth temperature for B. subtilis PTCC were 43.4 and 37°C, respectively (35). According to the results from a study on Bacillus sp., increase in temperature of isolates from 37 to 40°C led to an increase in lipase production (35). Golani reported the same optimum growth and lipase production temperature at 37°C by Staphylococcus chromogenes O1A (36). In the study reported by Yele, 12 medium components including MgCl2 and peptone were investigated by PB methods and were not determined as effective in the production of lipase by Staphylococcus warneri (18). Kai used PB method and reported that four factors including peptone and yeast extract have significant effects on lipase production by newly isolated Thalassospira permensis M35-15 (37).

There are similar reports on optimization of production processes using response surface methodology. The increment level in these studies is different and related to the bacterial strain and medium components and cultural conditions. Wang reported the isolation of T. permensis M35-15 from deep-sea water and 1.8 fold increase in lipase production by optimization of glucose, peptone, yeast powder and olive oil (38). Salihu et al. used sequential optimization strategy based on statistical experimental design including one-factor-at-a-time (OFAT) method and enhanced the 5.19-fold increase in the production of lipase by Candida cylindracea ATCC 14830 using palm oil mill effluent as a basal medium in shake flask cultures  (39). In another study, the Placket-Burman method was used for screening 10 medium factors and after optimization of effector factors using the Box–Behnken technique, a 9.1-fold enhancement in lipase production by Stenotrophomonas maltophilia was reported (40).  In our study, lipase production by I. zobellii showed a 2.09-fold increase by using Response Surface Methodology (RSM) for effective factors. In comparison to mentioned studies, increment level gained in our study is acceptable. The next step for more increase in lipase production by I. zobellii could be the replacement of ineffective factors with factors reported to be effective in similar or closely related bacterial species.

The results of our study and similar studies showed that there are no constant requirements for lipase production among different bacterial strains. Optimization studies similar to this study can reveal requirements of new bacterial species in the production process of high-value products and in addition to increase in process efficiency can decrease process costs. According to the advantages of alkaline enzymes, I. zobellii seems promising for biotechnological and industrial applications, which are conducted at alkaline reaction conditions.

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